options(knitr.duplicate.label = 'allow')
DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv") # make all variables (i.e. things that might change) as capital letters at the top of the scripts

ma_data <- read_csv(DATA_PATH) 
ma_data <- ma_data %>% filter(paradigm_type != "action_matching")

Data Overview

effect size & paper

n_effect_sizes <- ma_data %>%
  filter(!is.na(d_calc)) %>%
  nrow()

n_papers <- ma_data %>%
  distinct(unique_id) %>%
  nrow()

There are 16 effect sizes collected from 4 different papers.

Here are the papers in this analysis:

ma_data %>%
  count(short_cite) %>%
  arrange(-n) %>%
  DT::datatable()

## Forest plot

ma_model <- rma(ma_data$d_calc, ma_data$d_var_calc)
ma_model
## 
## Random-Effects Model (k = 16; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 6.1524 (SE = 2.3713)
## tau (square root of estimated tau^2 value):      2.4804
## I^2 (total heterogeneity / total variability):   97.75%
## H^2 (total variability / sampling variability):  44.46
## 
## Test for Heterogeneity:
## Q(df = 15) = 218.5173, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.6081  0.6386  0.9523  0.3410  -0.6435  1.8597    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
forest(ma_model,
       header = T,
       slab = ma_data$unique_id,
       col = "red",
       cex = .7)

## funnel plot {.tabset} ### vanilla

ma_data %>% 
  mutate(color = ifelse(sentence_structure == "transitive", "red", "blue"),
         color_sample_size = ifelse(n_1 < 10, "red", ifelse(n_1 < 20, "orange", "yellow")),
         color_confidence = ifelse(inclusion_certainty == 1, "red", "black"))-> ma_data_funnel


ma_data_funnel %>% filter (abs(d_calc) < 5) -> ma_data_funnel_no_outlier

ss_colors <- ma_data_funnel$color
ss_colors_no_outlier <- ma_data_funnel_no_outlier$color 

ma_model_funnel <- rma(ma_data_funnel$d_calc, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier <- rma(ma_data_funnel_no_outlier$d_calc, ma_data_funnel_no_outlier$d_var_calc)

f1<- funnel(ma_model_funnel, xlab = "Effect Size", col = ss_colors) 
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "All effect sizes break down by sentence structure")

f2<- funnel(ma_model_funnel_no_outlier, xlab = "Effect Size", col = ss_colors_no_outlier) 
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "effect sizes excluded outliers (abs <5) break down by sentence structure")

take into account sentence structure?

ma_model_sentence_structure <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure, ma_data_funnel$d_var_calc)
ma_model_no_outlier_ss <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure, ma_data_funnel_no_outlier$d_var_calc)

f3 <- funnel(ma_model_sentence_structure, xlab = "effect size", col = ss_colors)

f3_b <- funnel(ma_model_no_outlier_ss, xlab = "effect size", col = ss_colors_no_outlier)

take into account sentence structure and age?

ma_model_ss_age <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure + ma_data_funnel $mean_age, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier_ss_age <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure+ma_data_funnel_no_outlier$mean_age, ma_data_funnel_no_outlier$d_var_calc)



f4 <- funnel(ma_model_ss_age, xlab = "effect size", col = ss_colors)

f4_b <- funnel(ma_model_funnel_no_outlier_ss_age, xlab = "effect size", col = ss_colors_no_outlier)

Variable Summary

Continuous Variables

CONTINUOUS_VARS <- c("n_1", "x_1", "sd_1", "d_calc", "d_var_calc", "mean_age","n_train_test_pair","n_test_trial_per_pair","productive_vocab_mean", "productive_vocab_median")

long_continuous <- ma_data %>%
  pivot_longer(cols = CONTINUOUS_VARS)

long_continuous %>%
  ggplot(aes(x = value)) +
  geom_histogram() + 
  facet_wrap(~ name, scale = "free_x") +
  labs(title = "Distribution of continuous measures")

long_continuous %>%
  group_by(name) %>%
  summarize(mean = mean(value),
            sd = sd(value)) %>%
  kable()
name mean sd
d_calc 0.3329912 3.3611587
d_var_calc 0.4885109 1.1410342
mean_age NA NA
n_1 16.9375000 5.2341029
n_test_trial_per_pair 2.3750000 0.8062258
n_train_test_pair 2.8750000 1.5000000
productive_vocab_mean NA NA
productive_vocab_median NA NA
sd_1 0.0793202 0.0455952
x_1 0.5187500 0.1819313

Categorical Variables

CATEGORICAL_VARS <- c("sentence_structure", "agent_argument_type_clean", "patient_argument_type_clean", "stimuli_actor","agent_argument_number","transitive_event_type","intransitive_event_type",
                     "presentation_type", "character_identification",
                     "test_mass_or_distributed", "practice_phase", "test_method")

long_categorical <- ma_data %>%
  pivot_longer(cols = CATEGORICAL_VARS) %>%
  count(name, value) # this is a short cut for group_by() %>% summarize(count = n()) 

long_categorical %>%
  ggplot(aes(x = value, y = n)) +  
  facet_wrap(~ name, scale = "free_x") +
  geom_col(position = 'dodge',width=0.4) + 
  theme(text = element_text(size=8),
        axis.text.x = element_text(angle = 90, hjust = 1))  # rotate x-axis text

Prep for Moderators

ma_data_young <- ma_data_young <- ma_data %>%
    mutate(age_months = mean_age/30.44) %>% 
    filter(age_months < 36) 

ma_data_old <-  ma_data %>%
    mutate(age_months = mean_age/30.44) %>% 
    filter(age_months > 36 | age_months == 36) 

ma_data_vocab <- ma_data %>%
    filter(!is.na(productive_vocab_median)) 

no moderators

all

m1 <- rma.mv(d_calc, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m1)
## 
## Multivariate Meta-Analysis Model (k = 16; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -70.5838  141.1676  145.1676  146.5837  146.1676   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.2513  1.1186      4     no  short_cite 
## 
## Test for Heterogeneity:
## Q(df = 15) = 218.5173, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   1.0968  0.5675  1.9326  0.0533  -0.0155  2.2092  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

<36

m_young <- rma.mv(d_calc, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -31.1322   62.2644   66.2644   66.6588   68.2644   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.7519  1.3236      3     no  short_cite 
## 
## Test for Heterogeneity:
## Q(df = 9) = 128.0023, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.9857  0.7726  1.2757  0.2021  -0.5287  2.5000    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36

m_old <- rma.mv(d_calc, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -25.0728   50.1455   54.1455   52.9181   66.1455   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.0000  0.0000      2     no  short_cite 
## 
## Test for Heterogeneity:
## Q(df = 4) = 46.7325, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   1.4598  0.1861  7.8438  <.0001  1.0950  1.8246  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Participants characteristics:

age only

colored by unique_id

ma_data %>% 
  ggplot(aes(x = mean_age/30.44, y = d_calc,color = unique_id)) +
  geom_point() +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 

all

ma_data %>% 
  ggplot(aes(x = mean_age/30.44, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_smooth(color = "red") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
  theme(legend.position = "none") 

m_simple <- rma.mv(d_calc ~ 1, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

m_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_simple)
## 
## Multivariate Meta-Analysis Model (k = 16; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -70.5838  141.1676  145.1676  146.5837  146.1676   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.2513  1.1186      4     no  short_cite 
## 
## Test for Heterogeneity:
## Q(df = 15) = 218.5173, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   1.0968  0.5675  1.9326  0.0533  -0.0155  2.2092  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(m_age)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -58.4528  116.9057  122.9057  124.6005  125.5723   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.2883  1.5127      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 13) = 180.5581, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.0575, p-val = 0.0440
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -1.4262  1.4948  -0.9541  0.3400  -4.3559  1.5035    
## mean_age    0.0026  0.0013   2.0143  0.0440   0.0001  0.0052  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
forest(m_age,
       header = T,
       slab = ma_data$unique_id,
       col = "red",
       cex = .7
)

funnel(m_age)

young, < 36

ma_data_young %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_smooth(color = "red") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
  theme(legend.position = "none") 

m_age_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -29.4640   58.9279   64.9279   65.1663   70.9279   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.4152  1.1896      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 8) = 110.5672, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.3357, p-val = 0.2478
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     3.8533  2.5782   1.4946  0.1350  -1.1998  8.9064    
## mean_age   -0.0037  0.0032  -1.1557  0.2478  -0.0099  0.0026    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
forest(m_age_young,
       header = T,
       slab = ma_data_young$unique_id,
       col = "red",
       cex = .7
)

old, >= 36

ma_data_old %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_smooth(color = "red") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
  theme(legend.position = "none") 

m_age_old <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -23.1354   46.2708   52.2708   49.5667   76.2708   
## 
## Variance Components:
## 
##              estim    sqrt  nlvls  fixed      factor 
## sigma^2    18.3786  4.2870      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 46.1920, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7357, p-val = 0.0295
## 
## Model Results:
## 
##           estimate       se     zval    pval     ci.lb   ci.ub 
## intrcpt   -20.5753  10.5482  -1.9506  0.0511  -41.2493  0.0988  . 
## mean_age    0.0162   0.0074   2.1762  0.0295    0.0016  0.0307  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vocab

ma_data_vocab %>%
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_smooth(color = "red") +
  ylab("Effect Size") +
  xlab("Median Vocabulary Size") +
  ggtitle("Syntactical Bootstrapping effect size vs. Median Vocabulary (months)") +
  theme(legend.position = "none") 

m_age_vocab <- rma.mv(d_calc ~ productive_vocab_median, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_vocab)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.6288   11.2575   17.2575   15.4164   41.2575   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.8537  1.3615      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 51.4381, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1993, p-val = 0.6553
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                    1.9083  1.3230   1.4424  0.1492  -0.6848  4.5014    
## productive_vocab_median   -0.0078  0.0176  -0.4464  0.6553  -0.0423  0.0266    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_vocab_with_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_vocab_with_age)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.5064   11.0129   17.0129   15.1718   41.0129   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0957  1.4476      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 54.7221, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6046, p-val = 0.4368
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     3.4753  2.7320   1.2721  0.2033  -1.8793  8.8299    
## mean_age   -0.0028  0.0035  -0.7775  0.4368  -0.0097  0.0042    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linguistic Stimuli

Sentence structure

all with age

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 

m_age_sentence <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -29.4584   58.9167   66.9167   68.8564   72.6310   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.9134  0.9557      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 97.4627, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 60.8631, p-val < .0001
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                        -2.4912  1.1879  -2.0972  0.0360  -4.8193 
## mean_age                        0.0007  0.0011   0.6583  0.5103  -0.0014 
## sentence_structuretransitive    3.1601  0.4150   7.6155  <.0001   2.3468 
##                                 ci.ub 
## intrcpt                       -0.1630    * 
## mean_age                       0.0029      
## sentence_structuretransitive   3.9734  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_sentence <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -19.9833   39.9666   49.9666   51.9561   61.9666   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.1923  1.0919      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 82.0272, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 76.6859, p-val < .0001
## 
## Model Results:
## 
##                                        estimate      se     zval    pval 
## intrcpt                                 26.1561  7.2833   3.5912  0.0003 
## mean_age                                -0.0306  0.0079  -3.8607  0.0001 
## sentence_structuretransitive           -26.2584  7.3523  -3.5714  0.0004 
## mean_age:sentence_structuretransitive    0.0321  0.0080   4.0065  <.0001 
##                                           ci.lb     ci.ub 
## intrcpt                                 11.8811   40.4311  *** 
## mean_age                                -0.0461   -0.0151  *** 
## sentence_structuretransitive           -40.6686  -11.8481  *** 
## mean_age:sentence_structuretransitive    0.0164    0.0478  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

< 36, young, age

ma_data_young %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), with sentence_structure") 

m_age_sentence_young <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_sentence_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -8.6221   17.2443   25.2443   25.0279   45.2443   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.1806  1.0865      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 63.3586, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 42.9447, p-val < .0001
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                         1.1530  2.5577   0.4508  0.6521  -3.8600 
## mean_age                       -0.0034  0.0031  -1.0856  0.2777  -0.0095 
## sentence_structuretransitive    2.7606  0.4286   6.4408  <.0001   1.9206 
##                                ci.ub 
## intrcpt                       6.1660      
## mean_age                      0.0027      
## sentence_structuretransitive  3.6007  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_sentence_young <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_sentence_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -7.1583   14.3166   24.3166   23.2754   84.3166   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.1752  1.0841      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 60.9361, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 45.2491, p-val < .0001
## 
## Model Results:
## 
##                                        estimate       se     zval    pval 
## intrcpt                                 88.3469  57.5601   1.5349  0.1248 
## mean_age                                -0.0994   0.0634  -1.5681  0.1168 
## sentence_structuretransitive           -84.6169  57.6255  -1.4684  0.1420 
## mean_age:sentence_structuretransitive    0.0962   0.0635   1.5163  0.1294 
##                                            ci.lb     ci.ub 
## intrcpt                                 -24.4689  201.1626    
## mean_age                                 -0.2237    0.0248    
## sentence_structuretransitive           -197.5608   28.3270    
## mean_age:sentence_structuretransitive    -0.0282    0.2206    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old, age

ma_data_old %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 

m_age_sentence_old <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -7.3734   14.7468   22.7468   17.5193   62.7468   
## 
## Variance Components:
## 
##              estim    sqrt  nlvls  fixed      factor 
## sigma^2    22.2636  4.7184      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 18.1687, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 33.6609, p-val < .0001
## 
## Model Results:
## 
##                               estimate       se     zval    pval     ci.lb 
## intrcpt                       -32.6173  11.0000  -2.9652  0.0030  -54.1770 
## mean_age                        0.0164   0.0075   2.1768  0.0295    0.0016 
## sentence_structuretransitive   11.9559   2.2287   5.3646  <.0001    7.5878 
##                                  ci.ub 
## intrcpt                       -11.0577   ** 
## mean_age                        0.0312    * 
## sentence_structuretransitive   16.3241  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_sentence_old_interaction <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -7.3734   14.7468   22.7468   17.5193   62.7468   
## 
## Variance Components:
## 
##              estim    sqrt  nlvls  fixed      factor 
## sigma^2    22.2636  4.7184      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 18.1687, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 33.6609, p-val < .0001
## 
## Model Results:
## 
##                               estimate       se     zval    pval     ci.lb 
## intrcpt                       -32.6173  11.0000  -2.9652  0.0030  -54.1770 
## mean_age                        0.0164   0.0075   2.1768  0.0295    0.0016 
## sentence_structuretransitive   11.9559   2.2287   5.3646  <.0001    7.5878 
##                                  ci.ub 
## intrcpt                       -11.0577   ** 
## mean_age                        0.0312    * 
## sentence_structuretransitive   16.3241  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

all with vocab

ma_data_vocab %>% 
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = sentence_structure)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Median Productive Vocab") +
  ggtitle("Syntactical Bootstrapping effect size vs. Median Productive Vocab")

Agent Arguement Type (Noun, pronoun and varying)

decide to compare noun, pronoun and varying

ma_data %>% group_by(agent_argument_type_clean) %>% count()
## # A tibble: 2 x 2
## # Groups:   agent_argument_type_clean [2]
##   agent_argument_type_clean     n
##   <chr>                     <int>
## 1 noun                          4
## 2 pronoun                      12
ma_data_at <- ma_data %>% filter(agent_argument_type_clean != "noun_phrase")
ma_data_at_young <- ma_data_at %>%
    mutate(age_months = mean_age/30.44) %>% 
    filter(age_months < 36) 
ma_data_at_old <- ma_data_at %>%
    mutate(age_months = mean_age/30.44) %>% 
    filter(age_months > 36 | age_months == 36) 
ma_data_at_vocab <- ma_data_at %>%
      filter(!is.na(productive_vocab_median)) 

all

ma_data_at %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument") 

m_age_aa <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at)
summary(m_age_aa)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -57.6959  115.3919  123.3919  125.3315  129.1062   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.8811  1.6974      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 179.9297, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.8924, p-val = 0.0866
## 
## Model Results:
## 
##                                   estimate      se     zval    pval    ci.lb 
## intrcpt                            -1.8764  1.6226  -1.1565  0.2475  -5.0566 
## mean_age                            0.0029  0.0014   2.1208  0.0339   0.0002 
## agent_argument_type_cleanpronoun    0.3426  0.4557   0.7518  0.4522  -0.5506 
##                                    ci.ub 
## intrcpt                           1.3037    
## mean_age                          0.0056  * 
## agent_argument_type_cleanpronoun  1.2358    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_aa_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at)
summary(m_age_aa_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -53.0513  106.1027  116.1027  118.0922  128.1027   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    4.5123  2.1242      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 176.8145, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 14.1777, p-val = 0.0027
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     10.8558  4.8120   2.2560  0.0241 
## mean_age                                    -0.0149  0.0063  -2.3531  0.0186 
## agent_argument_type_cleanpronoun           -14.0663  4.9322  -2.8520  0.0043 
## mean_age:agent_argument_type_cleanpronoun    0.0193  0.0066   2.9445  0.0032 
##                                               ci.lb    ci.ub 
## intrcpt                                      1.4245  20.2872   * 
## mean_age                                    -0.0274  -0.0025   * 
## agent_argument_type_cleanpronoun           -23.7331  -4.3994  ** 
## mean_age:agent_argument_type_cleanpronoun    0.0065   0.0322  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

<36, young

ma_data_at_young %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, young only") 

m_age_aa_young <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at_young)
summary(m_age_aa_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -29.0641   58.1282   66.1282   65.9118   86.1282   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.5428  1.2421      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 101.5135, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.3567, p-val = 0.5074
## 
## Model Results:
## 
##                                   estimate      se     zval    pval    ci.lb 
## intrcpt                             3.5883  2.7415   1.3089  0.1906  -1.7849 
## mean_age                           -0.0034  0.0033  -1.0332  0.3015  -0.0099 
## agent_argument_type_cleanpronoun    0.1255  0.4653   0.2696  0.7874  -0.7865 
##                                    ci.ub 
## intrcpt                           8.9616    
## mean_age                          0.0031    
## agent_argument_type_cleanpronoun  1.0374    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_aa_young_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at_young)
summary(m_age_aa_young_interaction)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -26.3676   52.7351   62.7351   61.6939  122.7351   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.2854  1.8126      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 85.7429, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.8364, p-val = 0.1198
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     11.2717  4.7655   2.3653  0.0180 
## mean_age                                    -0.0144  0.0063  -2.2815  0.0225 
## agent_argument_type_cleanpronoun           -12.3929  5.7651  -2.1497  0.0316 
## mean_age:agent_argument_type_cleanpronoun    0.0170  0.0078   2.1826  0.0291 
##                                               ci.lb    ci.ub 
## intrcpt                                      1.9315  20.6119  * 
## mean_age                                    -0.0269  -0.0020  * 
## agent_argument_type_cleanpronoun           -23.6923  -1.0936  * 
## mean_age:agent_argument_type_cleanpronoun    0.0017   0.0323  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>=36, old

ma_data_at_old %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, young only") 

{r simple_model_age_aat_old} # m_age_aa_old <- rma.mv(d_calc ~ mean_age + agent_argument_type_clean, V = d_var_calc, # random = ~ 1 | short_cite, data = ma_data_at_old) # summary(m_age_aa_old) #

{r interaction_model_age_old} # m_age_aa_old_interaction <- rma.mv(d_calc ~ mean_age * agent_argument_type_clean, V = d_var_calc, # random = ~ 1 | short_cite, data = ma_data_at_old) # summary(m_age_aa_old_interaction) #

Vocab

ma_data_at_vocab %>% 
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = agent_argument_type_clean)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by agent argument, with vocab") 

m_age_aa_vocab <- rma.mv(d_calc ~ productive_vocab_median + agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at_vocab)
summary(m_age_aa_vocab)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.0814   10.1628   18.1628   14.5573   58.1628   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.6062  1.2673      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 32.8805, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6963, p-val = 0.7060
## 
## Model Results:
## 
##                                   estimate      se     zval    pval    ci.lb 
## intrcpt                             1.6986  1.3116   1.2951  0.1953  -0.8720 
## productive_vocab_median            -0.0053  0.0180  -0.2933  0.7693  -0.0405 
## agent_argument_type_cleanpronoun    0.3322  0.4742   0.7006  0.4835  -0.5971 
##                                    ci.ub 
## intrcpt                           4.2692    
## productive_vocab_median           0.0300    
## agent_argument_type_cleanpronoun  1.2615    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_aa_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * agent_argument_type_clean, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_at_vocab)
summary(m_age_aa_vocab_interaction)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -1.4686    2.9373   12.9373    6.4030   72.9373   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.2783  1.1306      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 20.6776, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.2171, p-val = 0.0653
## 
## Model Results:
## 
##                                                           estimate      se 
## intrcpt                                                     5.9771  2.0944 
## productive_vocab_median                                    -0.0870  0.0368 
## agent_argument_type_cleanpronoun                           -5.0090  2.1579 
## productive_vocab_median:agent_argument_type_cleanpronoun    0.1072  0.0422 
##                                                              zval    pval 
## intrcpt                                                    2.8539  0.0043 
## productive_vocab_median                                   -2.3653  0.0180 
## agent_argument_type_cleanpronoun                          -2.3212  0.0203 
## productive_vocab_median:agent_argument_type_cleanpronoun   2.5432  0.0110 
##                                                             ci.lb    ci.ub 
## intrcpt                                                    1.8722  10.0820  ** 
## productive_vocab_median                                   -0.1592  -0.0149   * 
## agent_argument_type_cleanpronoun                          -9.2384  -0.7796   * 
## productive_vocab_median:agent_argument_type_cleanpronoun   0.0246   0.1899   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Patient Argument Type (??)

ma_data %>% group_by(patient_argument_type_clean) %>% count()
## # A tibble: 4 x 2
## # Groups:   patient_argument_type_clean [4]
##   patient_argument_type_clean     n
##   <chr>                       <int>
## 1 intransitive                    4
## 2 noun                            5
## 3 noun_phrase                     3
## 4 pronoun                         4

Agent Argument Numbers (??)

ma_data %>% group_by(agent_argument_number) %>% count()
## # A tibble: 1 x 2
## # Groups:   agent_argument_number [1]
##   agent_argument_number     n
##   <chr>                 <int>
## 1 1                        16

Number of sentence reptition

ma_data %>% ggplot(aes(x = n_repetitions_sentence)) +
  geom_histogram() 

### all

ma_data %>% 
  ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("number of reptition in sentence") +
  ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition") 

m_rep <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_rep)
## 
## Multivariate Meta-Analysis Model (k = 16; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -67.4324  134.8648  140.8648  142.7820  143.2648   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.5197  0.7209      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 14) = 147.4682, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.8951, p-val = 0.0269
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                   3.7818  1.2742   2.9680  0.0030   1.2845   6.2792  ** 
## n_repetitions_sentence   -0.4294  0.1941  -2.2125  0.0269  -0.8098  -0.0490   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -56.2046  112.4091  120.4091  122.3488  126.1234   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.5811  1.8924      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 128.1286, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.6401, p-val = 0.0983
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                  -0.7678  3.8922  -0.1973  0.8436  -8.3964  6.8607    
## n_repetitions_sentence   -0.1440  0.5095  -0.2826  0.7775  -1.1426  0.8547    
## mean_age                  0.0029  0.0014   2.0098  0.0445   0.0001  0.0057  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age_interaction <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -51.8655  103.7311  113.7311  115.7206  125.7311   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.0000  0.0000      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 100.5837, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 84.2023, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## intrcpt                           13.2170  1.8064   7.3166  <.0001   9.6765 
## n_repetitions_sentence            -2.0554  0.3165  -6.4934  <.0001  -2.6758 
## mean_age                          -0.0086  0.0016  -5.5146  <.0001  -0.0117 
## n_repetitions_sentence:mean_age    0.0016  0.0003   5.2483  <.0001   0.0010 
##                                    ci.ub 
## intrcpt                          16.7576  *** 
## n_repetitions_sentence           -1.4350  *** 
## mean_age                         -0.0056  *** 
## n_repetitions_sentence:mean_age   0.0022  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

< 36, young only

ma_data_young %>% 
  ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("number of reptition in sentence") +
  ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition, young only") 

m_rep_young <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -27.8726   55.7452   61.7452   61.9835   67.7452   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.2950  0.5431      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 8) = 62.8092, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 9.5503, p-val = 0.0020
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                   5.4513  1.4904   3.6576  0.0003   2.5301   8.3724 
## n_repetitions_sentence   -0.6387  0.2067  -3.0904  0.0020  -1.0438  -0.2336 
##  
## intrcpt                 *** 
## n_repetitions_sentence   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age_young <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -27.3902   54.7805   62.7805   62.5641   82.7805   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.6001  0.7746      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 56.6214, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.3060, p-val = 0.0704
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                   6.1886  2.5236   2.4524  0.0142   1.2426  11.1347  * 
## n_repetitions_sentence   -0.5671  0.3209  -1.7676  0.0771  -1.1960   0.0617  . 
## mean_age                 -0.0016  0.0032  -0.4902  0.6240  -0.0079   0.0048    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age_interaction_young <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -26.0991   52.1981   62.1981   61.1569  122.1981   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.0000  0.0003      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 51.8353, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 76.1669, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## intrcpt                           18.5392  6.9010   2.6865  0.0072   5.0136 
## n_repetitions_sentence            -2.7701  0.9006  -3.0759  0.0021  -4.5353 
## mean_age                          -0.0155  0.0090  -1.7117  0.0870  -0.0331 
## n_repetitions_sentence:mean_age    0.0025  0.0011   2.1877  0.0287   0.0003 
##                                    ci.ub 
## intrcpt                          32.0648  ** 
## n_repetitions_sentence           -1.0050  ** 
## mean_age                          0.0022   . 
## n_repetitions_sentence:mean_age   0.0047   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old only

ma_data_old %>% 
  ggplot(aes(x = n_repetitions_sentence, y = d_calc, size = n_1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("number of reptition in sentence") +
  ggtitle("Syntactical Bootstrapping effect size vs. number of sentence repetition, old only") 

m_rep_old <- rma.mv(d_calc ~ n_repetitions_sentence, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -24.6833   49.3666   55.3666   52.6625   79.3666   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.6349  3.1040      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 46.6287, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0014, p-val = 0.9703
## 
## Model Results:
## 
##                         estimate      se     zval    pval     ci.lb    ci.ub 
## intrcpt                   1.6173  6.1366   0.2636  0.7921  -10.4101  13.6447    
## n_repetitions_sentence   -0.0329  0.8839  -0.0372  0.9703   -1.7652   1.6995    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age_old <- rma.mv(d_calc ~ n_repetitions_sentence + mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                 -34.0896  16.1013  -2.1172  0.0342  -65.6476  -2.5316 
## n_repetitions_sentence    1.3462   1.0478   1.2848  0.1989   -0.7075   3.4000 
## mean_age                  0.0197   0.0082   2.3948  0.0166    0.0036   0.0358 
##  
## intrcpt                 * 
## n_repetitions_sentence 
## mean_age                * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_age_interaction_old <- rma.mv(d_calc ~ n_repetitions_sentence * mean_age, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_interaction_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                 -34.0896  16.1013  -2.1172  0.0342  -65.6476  -2.5316 
## n_repetitions_sentence    1.3462   1.0478   1.2848  0.1989   -0.7075   3.4000 
## mean_age                  0.0197   0.0082   2.3948  0.0166    0.0036   0.0358 
##  
## intrcpt                 * 
## n_repetitions_sentence 
## mean_age                * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

add vocab in

m_rep_v_r <- rma.mv(d_calc ~ n_repetitions_sentence + productive_vocab_median, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_rep_v_r)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.4583    6.9166   14.9166   11.3111   54.9166   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1235  0.3514      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 7.0746, p-val = 0.0696
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.2593, p-val = 0.0022
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                    7.7187  1.8712   4.1250  <.0001   4.0512  11.3862 
## n_repetitions_sentence    -0.9738  0.2883  -3.3780  0.0007  -1.5388  -0.4088 
## productive_vocab_median   -0.0071  0.0176  -0.4014  0.6882  -0.0415   0.0274 
##  
## intrcpt                  *** 
## n_repetitions_sentence   *** 
## productive_vocab_median 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_rep_v_r_interaction <- rma.mv(d_calc ~ n_repetitions_sentence *productive_vocab_median, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_rep_v_r)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.4583    6.9166   14.9166   11.3111   54.9166   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1235  0.3514      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 7.0746, p-val = 0.0696
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.2593, p-val = 0.0022
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                    7.7187  1.8712   4.1250  <.0001   4.0512  11.3862 
## n_repetitions_sentence    -0.9738  0.2883  -3.3780  0.0007  -1.5388  -0.4088 
## productive_vocab_median   -0.0071  0.0176  -0.4014  0.6882  -0.0415   0.0274 
##  
## intrcpt                  *** 
## n_repetitions_sentence   *** 
## productive_vocab_median 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Visual stimuli

stimuli_modality(video vs animation)

all

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality") 

m_stim_mod<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_mod)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -56.5931  113.1862  121.1862  123.1258  126.9005   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.9820  1.9955      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 172.7654, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.6822, p-val = 0.0962
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                 -1.6445  2.2452  -0.7325  0.4639  -6.0450  2.7560    
## mean_age                 0.0031  0.0015   2.1320  0.0330   0.0003  0.0060  * 
## stimuli_modalityvideo   -0.3061  2.3757  -0.1288  0.8975  -4.9623  4.3502    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_mod_interaction<- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_stim_mod_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -57.1955  114.3910  124.3910  126.3805  136.3910   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.9761  1.9940      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 172.7393, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 4.6845, p-val = 0.1964
## 
## Model Results:
## 
##                                 estimate       se     zval    pval     ci.lb 
## intrcpt                          -2.9504  21.4977  -0.1372  0.8908  -45.0852 
## mean_age                          0.0050   0.0307   0.1624  0.8710   -0.0551 
## stimuli_modalityvideo             1.0054  21.5856   0.0466  0.9628  -41.3015 
## mean_age:stimuli_modalityvideo   -0.0019   0.0307  -0.0611  0.9513   -0.0620 
##                                   ci.ub 
## intrcpt                         39.1844    
## mean_age                         0.0651    
## stimuli_modalityvideo           43.3124    
## mean_age:stimuli_modalityvideo   0.0583    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

< 36, young only

ma_data_young %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, young") 

m_stim_mod_young<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_mod_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -27.6415   55.2829   63.2829   63.0666   83.2829   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.7770  1.3330      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 59.6530, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7056, p-val = 0.4262
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                  3.4120  2.6939   1.2666  0.2053  -1.8680  8.6921    
## mean_age                -0.0041  0.0033  -1.2360  0.2164  -0.0107  0.0024    
## stimuli_modalityvideo    1.1958  1.6962   0.7050  0.4808  -2.1287  4.5203    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_mod_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_stim_mod_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.1963   56.3926   66.3926   65.3514  126.3926   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.7348  1.3171      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 59.3453, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.8212, p-val = 0.6103
## 
## Model Results:
## 
##                                 estimate       se     zval    pval     ci.lb 
## intrcpt                          -2.9504  21.4456  -0.1376  0.8906  -44.9829 
## mean_age                          0.0050   0.0307   0.1624  0.8710   -0.0551 
## stimuli_modalityvideo             7.6679  21.6423   0.3543  0.7231  -34.7503 
## mean_age:stimuli_modalityvideo   -0.0092   0.0308  -0.2998  0.7643   -0.0697 
##                                   ci.ub 
## intrcpt                         39.0821    
## mean_age                         0.0651    
## stimuli_modalityvideo           50.0860    
## mean_age:stimuli_modalityvideo   0.0512    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old only

ma_data_old %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, old") 

vocab

ma_data_vocab %>% 
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = stimuli_modality)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("median vocab") +
  ggtitle("Syntactical Bootstrapping effect size vs. median vocab, breakdown by stimuli modality, vocab") 

m_stim_mod_v <- rma.mv(d_calc ~ productive_vocab_median + stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_v)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.4583    6.9166   14.9166   11.3111   54.9166   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1235  0.3514      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 7.0746, p-val = 0.0696
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 12.2593, p-val = 0.0022
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                    0.9023  1.0159   0.8881  0.3745  -1.0889  2.8935 
## productive_vocab_median   -0.0071  0.0176  -0.4014  0.6882  -0.0415  0.0274 
## stimuli_modalityvideo      1.9476  0.5765   3.3780  0.0007   0.8175  3.0776 
##  
## intrcpt 
## productive_vocab_median 
## stimuli_modalityvideo    *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_mod_intereaction_v <- rma.mv(d_calc ~ productive_vocab_median * stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_intereaction_v)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -4.0472    8.0945   18.0945   11.5602   78.0945   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1237  0.3517      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 7.0656, p-val = 0.0292
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 12.2511, p-val = 0.0066
## 
## Model Results:
## 
##                                                estimate      se     zval 
## intrcpt                                          1.4167  5.5073   0.2572 
## productive_vocab_median                         -0.0167  0.1027  -0.1624 
## stimuli_modalityvideo                            1.4191  5.5910   0.2538 
## productive_vocab_median:stimuli_modalityvideo    0.0099  0.1042   0.0950 
##                                                  pval    ci.lb    ci.ub 
## intrcpt                                        0.7970  -9.3774  12.2107    
## productive_vocab_median                        0.8710  -0.2179   0.1845    
## stimuli_modalityvideo                          0.7996  -9.5390  12.3771    
## productive_vocab_median:stimuli_modalityvideo  0.9243  -0.1943   0.2141    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vocab age comparison

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_modality)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli modality, age compare vocab") 

m_stim_mod_v_a<- rma.mv(d_calc ~ mean_age + stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_v_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.2187    6.4373   14.4373   10.8318   54.4373   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1094  0.3308      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 6.5517, p-val = 0.0876
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 13.9246, p-val = 0.0009
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                  2.5762  2.5072   1.0275  0.3042  -2.3378  7.4901      
## mean_age                -0.0029  0.0036  -0.8270  0.4082  -0.0099  0.0040      
## stimuli_modalityvideo    2.0689  0.5547   3.7294  0.0002   0.9816  3.1561  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_mod_interaction_v_a <- rma.mv(d_calc ~ mean_age * stimuli_modality, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_stim_mod_interaction_v_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.7849    7.5698   17.5698   11.0356   77.5698   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.1088  0.3299      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 6.4842, p-val = 0.0391
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 14.0474, p-val = 0.0028
## 
## Model Results:
## 
##                                 estimate       se     zval    pval     ci.lb 
## intrcpt                          -2.9504  21.4076  -0.1378  0.8904  -44.9086 
## mean_age                          0.0050   0.0307   0.1624  0.8710   -0.0551 
## stimuli_modalityvideo             7.6739  21.5690   0.3558  0.7220  -34.6006 
## mean_age:stimuli_modalityvideo   -0.0080   0.0309  -0.2599  0.7949   -0.0685 
##                                   ci.ub 
## intrcpt                         39.0077    
## mean_age                         0.0651    
## stimuli_modalityvideo           49.9483    
## mean_age:stimuli_modalityvideo   0.0525    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

stimuli_actor (person vs non-person)

all

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, all") 

m_stim_actor_age <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

summary(m_stim_actor_age)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -55.8743  111.7485  119.7485  121.6882  125.4628   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    4.5635  2.1362      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 175.1689, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 8.5454, p-val = 0.0139
## 
## Model Results:
## 
##                      estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt               -3.4879  1.9468  -1.7916  0.0732  -7.3036  0.3278   . 
## mean_age               0.0044  0.0016   2.7916  0.0052   0.0013  0.0075  ** 
## stimuli_actorperson    1.0343  0.5339   1.9372  0.0527  -0.0121  2.0808   . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_actor_age_interaction <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

summary(m_stim_actor_age_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -54.3997  108.7994  118.7994  120.7889  130.7994   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    5.7049  2.3885      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 161.4404, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 9.0029, p-val = 0.0293
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                        -3.1994  3.2878  -0.9731  0.3305  -9.6434 
## mean_age                        0.0041  0.0032   1.2655  0.2057  -0.0022 
## stimuli_actorperson             0.5305  2.7608   0.1921  0.8476  -4.8807 
## mean_age:stimuli_actorperson    0.0007  0.0036   0.2003  0.8413  -0.0063 
##                                ci.ub 
## intrcpt                       3.2446    
## mean_age                      0.0104    
## stimuli_actorperson           5.9416    
## mean_age:stimuli_actorperson  0.0078    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

< 36, young only

ma_data_young %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, young") 

m_stim_actor_age_young <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)

summary(m_stim_actor_age_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.4463   56.8926   64.8926   64.6763   84.8926   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.3823  1.5435      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 108.8621, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.0896, p-val = 0.5800
## 
## Model Results:
## 
##                      estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                4.0058  4.9490   0.8094  0.4183  -5.6941  13.7056    
## mean_age              -0.0038  0.0058  -0.6565  0.5115  -0.0153   0.0076    
## stimuli_actorperson   -0.0689  0.8519  -0.0809  0.9355  -1.7386   1.6008    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_actor_age_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)

summary(m_stim_actor_age_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -26.8142   53.6283   63.6283   62.5871  123.6283   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.3555  1.1643      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 53.3803, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.2000, p-val = 0.5320
## 
## Model Results:
## 
##                               estimate       se     zval    pval     ci.lb 
## intrcpt                        -6.8631  13.1352  -0.5225  0.6013  -32.6076 
## mean_age                        0.0111   0.0178   0.6247  0.5322   -0.0238 
## stimuli_actorperson            13.8307  15.5787   0.8878  0.3747  -16.7031 
## mean_age:stimuli_actorperson   -0.0186   0.0208  -0.8966  0.3699   -0.0593 
##                                 ci.ub 
## intrcpt                       18.8815    
## mean_age                       0.0461    
## stimuli_actorperson           44.3644    
## mean_age:stimuli_actorperson   0.0221    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old only

ma_data_old %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, old") 

m_stim_actor_age_old <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)

summary(m_stim_actor_age_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                      estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt              -28.7046  12.9777  -2.2118  0.0270  -54.1404  -3.2688  * 
## mean_age               0.0197   0.0082   2.3948  0.0166    0.0036   0.0358  * 
## stimuli_actorperson    6.7312   5.2392   1.2848  0.1989   -3.5374  16.9998    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_actor_age_interaction_old <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)

summary(m_stim_actor_age_interaction_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                      estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt              -28.7046  12.9777  -2.2118  0.0270  -54.1404  -3.2688  * 
## mean_age               0.0197   0.0082   2.3948  0.0166    0.0036   0.0358  * 
## stimuli_actorperson    6.7312   5.2392   1.2848  0.1989   -3.5374  16.9998    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vocab

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = stimuli_actor)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, vocab") 

m_stim_actor_vocab <- rma.mv(d_calc ~ productive_vocab_median + stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_stim_actor_vocab)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -3.0526    6.1052   14.1052   10.4997   54.1052   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.8983  0.9478      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 15.2192, p-val = 0.0016
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.0929, p-val = 0.0784
## 
## Model Results:
## 
##                          estimate      se     zval    pval     ci.lb   ci.ub 
## intrcpt                   -5.1831  3.4257  -1.5130  0.1303  -11.8973  1.5310    
## productive_vocab_median    0.1190  0.0605   1.9652  0.0494    0.0003  0.2376  * 
## stimuli_actorperson        3.7386  1.6975   2.2024  0.0276    0.4115  7.0657  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_actor_vocab_interaction <- rma.mv(d_calc ~ productive_vocab_median * stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_stim_actor_vocab_interaction)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -1.8145    3.6289   13.6289    7.0947   73.6289   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.0695  1.0342      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 14.8974, p-val = 0.0006
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.5762, p-val = 0.0867
## 
## Model Results:
## 
##                                              estimate      se     zval    pval 
## intrcpt                                       -3.5249  3.6548  -0.9645  0.3348 
## productive_vocab_median                        0.0891  0.0645   1.3811  0.1673 
## stimuli_actorperson                           -4.5120  6.5909  -0.6846  0.4936 
## productive_vocab_median:stimuli_actorperson    0.2442  0.1897   1.2877  0.1978 
##                                                 ci.lb   ci.ub 
## intrcpt                                      -10.6882  3.6384    
## productive_vocab_median                       -0.0374  0.2156    
## stimuli_actorperson                          -17.4299  8.4058    
## productive_vocab_median:stimuli_actorperson   -0.1275  0.6159    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vocab age comparison

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by stimuli actor, age vocab comparison") 

m_stim_actor_vocab_a <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_stim_actor_vocab_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -2.8031    5.6062   13.6062   10.0007   53.6062   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.0000  0.0000      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 5.7871, p-val = 0.1224
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 51.8748, p-val < .0001
## 
## Model Results:
## 
##                      estimate      se     zval    pval     ci.lb     ci.ub 
## intrcpt              -16.4159  3.0466  -5.3882  <.0001  -22.3872  -10.4446  *** 
## mean_age               0.0243  0.0043   5.7021  <.0001    0.0159    0.0326  *** 
## stimuli_actorperson    3.8266  0.5470   6.9954  <.0001    2.7545    4.8988  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_stim_actor_vocab_interaction_a <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_stim_actor_vocab_interaction_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -1.4159    2.8317   12.8317    6.2974   72.8317   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    0.3194  0.5651      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 3.9127, p-val = 0.1414
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.9325, p-val = 0.0302
## 
## Model Results:
## 
##                               estimate       se     zval    pval      ci.lb 
## intrcpt                       -13.4712   7.8773  -1.7101  0.0872   -28.9105 
## mean_age                        0.0203   0.0107   1.8867  0.0592    -0.0008 
## stimuli_actorperson           -40.8319  31.5812  -1.2929  0.1960  -102.7298 
## mean_age:stimuli_actorperson    0.0695   0.0492   1.4124  0.1578    -0.0269 
##                                 ci.ub 
## intrcpt                        1.9680  . 
## mean_age                       0.0413  . 
## stimuli_actorperson           21.0660    
## mean_age:stimuli_actorperson   0.1659    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

transitive_event (indirect_caused_action vs direct caused action)

ma_data %>% count(transitive_event_type)
## # A tibble: 1 x 2
##   transitive_event_type     n
##   <chr>                 <int>
## 1 AGENT                    16
m_data_transtivity <- ma_data %>% 
  filter(transitive_event_type != "minimal_contact") 

m_data_transtivity_young <- ma_data %>% 
  filter(transitive_event_type != "minimal_contact") %>% 
  mutate(age_months = mean_age/30.44) %>% 
  filter(age_months < 36) 

m_data_transtivity_old <- ma_data %>% 
  filter(transitive_event_type != "minimal_contact") %>% 
  mutate(age_months = mean_age/30.44) %>% 
  filter(age_months > 36 | age_months == 36) 

m_data_transitivity_vocab <- ma_data_vocab %>% 
    filter(transitive_event_type != "minimal_contact") 

all

m_data_transtivity %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = transitive_event_type)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by transitive_event_type") 

intransitive_event (one vs parallel)

ma_data %>% count(intransitive_event_type) 
## # A tibble: 1 x 2
##   intransitive_event_type     n
##   <chr>                   <int>
## 1 AGENT                      16
m_data_intran <- ma_data %>% 
  filter(intransitive_event_type != "minimal_contact") 

m_data_intran_young <- ma_data %>% 
  filter(intransitive_event_type != "minimal_contact") %>% 
  mutate(age_months = mean_age/30.44) %>% 
  filter(age_months < 36) 

m_data_intran_old <- ma_data %>% 
  filter(intransitive_event_type != "minimal_contact") %>% 
  mutate(age_months = mean_age/30.44) %>% 
  filter(age_months > 36 | age_months == 36) 

m_data_intran_vocab <- ma_data_vocab %>% 
    filter(intransitive_event_type != "minimal_contact") 

all

m_data_intran %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = intransitive_event_type)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by intransitive_event_type") 

maybe need to look at the combination of transitive vs intransitive?

ma_data %>% group_by(transitive_event_type, intransitive_event_type) %>% count()
## # A tibble: 1 x 3
## # Groups:   transitive_event_type, intransitive_event_type [1]
##   transitive_event_type intransitive_event_type     n
##   <chr>                 <chr>                   <int>
## 1 AGENT                 AGENT                      16

n_repetition_video (??)

ma_data %>% ggplot(aes(x = n_repetitions_video)) +
  geom_histogram() 

Testing Procedure

test_method (point vs look) (???)

ma_data %>% count(test_method)
## # A tibble: 2 x 2
##   test_method     n
##   <chr>       <int>
## 1 look            6
## 2 point          10

presentation_type (asynchronous vs immediate after )(maybe collapsing simultaneous with immediate after?)

ma_data %>% count(presentation_type)
## # A tibble: 1 x 2
##   presentation_type     n
##   <chr>             <int>
## 1 immediate_after      16
ma_data_pt <- ma_data %>% 
  filter(presentation_type != "simultaneous") 

ma_data_pt_young <- ma_data_pt %>% 
  mutate(age_months = mean_age/30.44) %>% 
    filter(age_months < 36) 

ma_data_pt_old <- ma_data_pt %>% 
  mutate(age_months = mean_age/30.44) %>% 
    filter(age_months > 36 | age_months == 36) 

ma_data_pt_vocab <- ma_data_vocab %>% 
    filter(presentation_type != "simultaneous") 

all

ma_data_pt %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1,  color = presentation_type)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by presentation type") 

character_identification

ma_data %>% count(character_identification)
## # A tibble: 2 x 2
##   character_identification     n
##   <chr>                    <int>
## 1 no                           7
## 2 yes                          9

all

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification") 

m_age_ci <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

summary(m_age_ci)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -56.0340  112.0680  120.0680  122.0076  125.7823   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.2120  1.7922      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 154.5945, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.8078, p-val = 0.0904
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                       -2.6048  2.2311  -1.1675  0.2430  -6.9777  1.7681 
## mean_age                       0.0029  0.0014   2.0943  0.0362   0.0002  0.0056 
## character_identificationyes    1.2510  2.0812   0.6011  0.5478  -2.8282  5.3302 
##  
## intrcpt 
## mean_age                     * 
## character_identificationyes 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_stimuli_ci_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_age_stimuli_ci_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -53.6799  107.3597  117.3597  119.3492  129.3597   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.5416  1.2416      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 148.8517, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.1275, p-val = 0.0434
## 
## Model Results:
## 
##                                       estimate      se     zval    pval 
## intrcpt                                -4.6041  2.1667  -2.1249  0.0336 
## mean_age                                0.0050  0.0019   2.6927  0.0071 
## character_identificationyes             5.9212  2.7908   2.1217  0.0339 
## mean_age:character_identificationyes   -0.0048  0.0025  -1.9509  0.0511 
##                                         ci.lb    ci.ub 
## intrcpt                               -8.8508  -0.3574   * 
## mean_age                               0.0014   0.0086  ** 
## character_identificationyes            0.4514  11.3911   * 
## mean_age:character_identificationyes  -0.0096   0.0000   . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

<36, young

ma_data_young %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification, young only") 

m_age_ci_young <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)

summary(m_age_ci_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.3250   56.6500   64.6500   64.4337   84.6500   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0997  1.4490      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 100.0259, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4077, p-val = 0.4947
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                        2.5154  3.5290   0.7128  0.4760  -4.4013  9.4321 
## mean_age                      -0.0028  0.0035  -0.8014  0.4229  -0.0097  0.0041 
## character_identificationyes    1.0084  1.9222   0.5246  0.5999  -2.7590  4.7757 
##  
## intrcpt 
## mean_age 
## character_identificationyes 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_ci_interaction_young <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_ci_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.3118   56.6236   66.6236   65.5824  126.6236   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0957  1.4476      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 99.8371, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.4401, p-val = 0.6962
## 
## Model Results:
## 
##                                       estimate       se     zval    pval 
## intrcpt                                 7.7218  29.7396   0.2596  0.7951 
## mean_age                               -0.0085   0.0326  -0.2619  0.7934 
## character_identificationyes            -4.2465  29.8649  -0.1422  0.8869 
## mean_age:character_identificationyes    0.0058   0.0328   0.1763  0.8600 
##                                          ci.lb    ci.ub 
## intrcpt                               -50.5668  66.0105    
## mean_age                               -0.0725   0.0554    
## character_identificationyes           -62.7806  54.2875    
## mean_age:character_identificationyes   -0.0585   0.0701    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old

ma_data_old %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
  geom_point() +
   geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by character_identification, old only") 

m_age_ci_old <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)

summary(m_age_ci_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                              estimate       se     zval    pval     ci.lb 
## intrcpt                      -21.9734  10.2122  -2.1517  0.0314  -41.9890 
## mean_age                       0.0197   0.0082   2.3948  0.0166    0.0036 
## character_identificationyes   -6.7312   5.2392  -1.2848  0.1989  -16.9998 
##                                ci.ub 
## intrcpt                      -1.9579  * 
## mean_age                      0.0358  * 
## character_identificationyes   3.5375    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_ci_interaction_old <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_ci_interaction_old)
## 
## Multivariate Meta-Analysis Model (k = 5; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -21.7925   43.5851   51.5851   46.3577   91.5851   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    9.4487  3.0739      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 40.8937, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.7364, p-val = 0.0568
## 
## Model Results:
## 
##                              estimate       se     zval    pval     ci.lb 
## intrcpt                      -21.9734  10.2122  -2.1517  0.0314  -41.9890 
## mean_age                       0.0197   0.0082   2.3948  0.0166    0.0036 
## character_identificationyes   -6.7312   5.2392  -1.2848  0.1989  -16.9998 
##                                ci.ub 
## intrcpt                      -1.9579  * 
## mean_age                      0.0358  * 
## character_identificationyes   3.5375    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

vocab

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = character_identification)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("median vocab") +
  ggtitle("Syntactical Bootstrapping effect size vs. median voca, breakdown by character_identification, old only") 

practice_phase

all

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase") 

m_age_pf <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

summary(m_age_ci)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -56.0340  112.0680  120.0680  122.0076  125.7823   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    3.2120  1.7922      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 154.5945, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 4.8078, p-val = 0.0904
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                       -2.6048  2.2311  -1.1675  0.2430  -6.9777  1.7681 
## mean_age                       0.0029  0.0014   2.0943  0.0362   0.0002  0.0056 
## character_identificationyes    1.2510  2.0812   0.6011  0.5478  -2.8282  5.3302 
##  
## intrcpt 
## mean_age                     * 
## character_identificationyes 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_pf_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_age_stimuli_ci_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -53.6799  107.3597  117.3597  119.3492  129.3597   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.5416  1.2416      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 148.8517, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 8.1275, p-val = 0.0434
## 
## Model Results:
## 
##                                       estimate      se     zval    pval 
## intrcpt                                -4.6041  2.1667  -2.1249  0.0336 
## mean_age                                0.0050  0.0019   2.6927  0.0071 
## character_identificationyes             5.9212  2.7908   2.1217  0.0339 
## mean_age:character_identificationyes   -0.0048  0.0025  -1.9509  0.0511 
##                                         ci.lb    ci.ub 
## intrcpt                               -8.8508  -0.3574   * 
## mean_age                               0.0014   0.0086  ** 
## character_identificationyes            0.4514  11.3911   * 
## mean_age:character_identificationyes  -0.0096   0.0000   . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

<36, young

ma_data_young %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, young only") 

m_age_pf_young <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)

summary(m_age_pf_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -29.5512   59.1025   67.1025   66.8861   87.1025   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.2976  1.1391      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 91.2605, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.6345, p-val = 0.4416
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt              3.8596  2.5519   1.5124  0.1304  -1.1420  8.8613    
## mean_age            -0.0038  0.0032  -1.1972  0.2312  -0.0100  0.0024    
## practice_phaseyes    0.1490  0.3029   0.4920  0.6227  -0.4446  0.7426    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_pf_interaction_young <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_pf_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.6312   57.2625   67.2625   66.2213  127.2625   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.9011  1.3788      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 89.2202, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.6196, p-val = 0.6550
## 
## Model Results:
## 
##                             estimate      se     zval    pval     ci.lb 
## intrcpt                       6.4798  5.9731   1.0848  0.2780   -5.2273 
## mean_age                     -0.0072  0.0077  -0.9340  0.3503   -0.0223 
## practice_phaseyes            -2.8830  5.8906  -0.4894  0.6245  -14.4284 
## mean_age:practice_phaseyes    0.0043  0.0084   0.5121  0.6086   -0.0122 
##                               ci.ub 
## intrcpt                     18.1869    
## mean_age                     0.0079    
## practice_phaseyes            8.6623    
## mean_age:practice_phaseyes   0.0208    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old

ma_data_old %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, old only") 

vocab

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = practice_phase)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("productive vocab") +
  ggtitle("Syntactical Bootstrapping effect size vs. productive vocab, breakdown by practice_phase, old only") 

m_age_pf_vocab <- rma.mv(d_calc ~ productive_vocab_median + practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_age_pf_vocab)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.9892   11.9784   19.9784   16.3729   59.9784   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.8171  1.3480      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 42.3702, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2288, p-val = 0.8919
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                    1.8427  1.3729   1.3422  0.1795  -0.8481  4.5335    
## productive_vocab_median   -0.0073  0.0178  -0.4110  0.6811  -0.0423  0.0276    
## practice_phaseyes          0.0528  0.3117   0.1694  0.8655  -0.5582  0.6638    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_pf_interaction_vocab <- rma.mv(d_calc ~ productive_vocab_median * practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pf_interaction_vocab)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.9892   11.9784   19.9784   16.3729   59.9784   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.8171  1.3480      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 42.3702, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2288, p-val = 0.8919
## 
## Model Results:
## 
##                          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                    1.8427  1.3729   1.3422  0.1795  -0.8481  4.5335    
## productive_vocab_median   -0.0073  0.0178  -0.4110  0.6811  -0.0423  0.0276    
## practice_phaseyes          0.0528  0.3117   0.1694  0.8655  -0.5582  0.6638    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

age w/ vocab comparsion

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = mean_age, y = d_calc, size = n_1, color = practice_phase)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("mean age") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by practice_phase, old only") 

m_age_pf_vocab_a <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)

summary(m_age_pf_vocab_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.8008   11.6016   19.6016   15.9961   59.6016   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0081  1.4171      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 44.0717, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7115, p-val = 0.7007
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt              3.4940  2.7248   1.2823  0.1997  -1.8466  8.8345    
## mean_age            -0.0029  0.0036  -0.8095  0.4182  -0.0099  0.0041    
## practice_phaseyes    0.1027  0.3093   0.3320  0.7399  -0.5035  0.7088    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_pf_interaction_vocab_a <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_vocab)
summary(m_age_pf_interaction_vocab_a)
## 
## Multivariate Meta-Analysis Model (k = 6; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -5.8008   11.6016   19.6016   15.9961   59.6016   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0081  1.4171      2     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 3) = 44.0717, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7115, p-val = 0.7007
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt              3.4940  2.7248   1.2823  0.1997  -1.8466  8.8345    
## mean_age            -0.0029  0.0036  -0.8095  0.4182  -0.0099  0.0041    
## practice_phaseyes    0.1027  0.3093   0.3320  0.7399  -0.5035  0.7088    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

test_mass_distributed

all

ma_data %>% 
  mutate(age_months = mean_age/30.44) %>% 
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test type") 

m_age_tt <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)

summary(m_age_tt)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -55.3701  110.7401  118.7401  120.6797  124.4544   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.1332  1.0645      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 12) = 148.1928, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 6.9802, p-val = 0.0305
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                        -3.5605  1.8703  -1.9037  0.0569  -7.2262 
## mean_age                        0.0035  0.0014   2.5647  0.0103   0.0008 
## test_mass_or_distributedmass    2.5529  1.3042   1.9575  0.0503  -0.0032 
##                                ci.ub 
## intrcpt                       0.1051  . 
## mean_age                      0.0062  * 
## test_mass_or_distributedmass  5.1090  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_tt_interaction <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data)
summary(m_age_tt_interaction)
## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -53.2590  106.5180  116.5180  118.5075  128.5180   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    1.8313  1.3533      4     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 147.2843, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.4552, p-val = 0.0151
## 
## Model Results:
## 
##                                        estimate      se     zval    pval 
## intrcpt                                 -5.3020  2.1983  -2.4119  0.0159 
## mean_age                                 0.0049  0.0016   3.0994  0.0019 
## test_mass_or_distributedmass             8.7583  3.4873   2.5115  0.0120 
## mean_age:test_mass_or_distributedmass   -0.0077  0.0039  -1.9699  0.0488 
##                                          ci.lb    ci.ub 
## intrcpt                                -9.6106  -0.9935   * 
## mean_age                                0.0018   0.0080  ** 
## test_mass_or_distributedmass            1.9233  15.5933   * 
## mean_age:test_mass_or_distributedmass  -0.0153  -0.0000   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

<36, young

ma_data_young %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test_mass_or_distributed, young only") 

m_age_tt_young <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)

summary(m_age_tt_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.3250   56.6500   64.6500   64.4337   84.6500   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0997  1.4490      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 7) = 100.0259, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4077, p-val = 0.4947
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                         2.5154  3.5290   0.7128  0.4760  -4.4013 
## mean_age                       -0.0028  0.0035  -0.8014  0.4229  -0.0097 
## test_mass_or_distributedmass    1.0084  1.9222   0.5246  0.5999  -2.7590 
##                                ci.ub 
## intrcpt                       9.4321    
## mean_age                      0.0041    
## test_mass_or_distributedmass  4.7757    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m_age_tt_interaction_young <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
                            random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_tt_interaction_young)
## 
## Multivariate Meta-Analysis Model (k = 10; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -28.3118   56.6236   66.6236   65.5824  126.6236   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed      factor 
## sigma^2    2.0957  1.4476      3     no  short_cite 
## 
## Test for Residual Heterogeneity:
## QE(df = 6) = 99.8371, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.4401, p-val = 0.6962
## 
## Model Results:
## 
##                                        estimate       se     zval    pval 
## intrcpt                                  7.7218  29.7396   0.2596  0.7951 
## mean_age                                -0.0085   0.0326  -0.2619  0.7934 
## test_mass_or_distributedmass            -4.2465  29.8649  -0.1422  0.8869 
## mean_age:test_mass_or_distributedmass    0.0058   0.0328   0.1763  0.8600 
##                                           ci.lb    ci.ub 
## intrcpt                                -50.5668  66.0105    
## mean_age                                -0.0725   0.0554    
## test_mass_or_distributedmass           -62.7806  54.2875    
## mean_age:test_mass_or_distributedmass   -0.0585   0.0701    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

>= 36, old

ma_data_old %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("Age (months)") +
  ggtitle("Syntactical Bootstrapping effect size vs. Age (months), breakdown by test_mass_or_distributed, old only") 

vocab

ma_data_vocab %>% 
  mutate(age_months = mean_age/30.44) %>%
  ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
  geom_point() +
  geom_smooth(method = "lm") +
  ylab("Effect Size") +
  xlab("median vocab") +
  ggtitle("Syntactical Bootstrapping effect size vs. median voca, breakdown by test_mass_or_distributed, old only") 

n_train_test_pair (????)

ma_data %>% ggplot(aes(x = n_train_test_pair)) +
  geom_histogram() 

n_test_trial (???)

ma_data %>% ggplot(aes(x = n_test_trial_per_pair)) +
  geom_histogram()